code: def __init__(self, args: dict):
      BaseDecoder.__init__(self)
  
      self.input_dim = args.input_dim
  
      layers = nn.ModuleList()
  
      layers.append(
          nn.Sequential(
              GatedDense(args.latent_dim, 300), GatedDense(300, 300)
          )
      )
  
      layers.append(
          nn.Sequential(
              NonLinear(
                  300, np.prod(args.input_dim), activation=nn.Sigmoid()
              )
          )
      )
  
      self.layers = layers
      self.depth = len(layers)
  
      for m in self.modules():
          if isinstance(m, nn.Linear):
              he_init(m)
  
imports: import logging
  import os
  from typing import List
  import numpy
  import torch
  import torch.nn
  from pythae.data.preprocessors import DataProcessor
  from pythae.models import VAMP, AutoModel, VAMPConfig
  from pythae.models.base.base_utils import ModelOutput
  from pythae.models.nn import BaseDecoder, BaseEncoder
  from pythae.trainers import BaseTrainer, BaseTrainerConfig
label_desc: Use image node_id 1 to create a new Decoder object from inherited base classes: BaseDecoder with example: obj = Decoder(args)